Real-Time Globally Consistent Dense 3D Reconstruction With Online Texturing

Lei Han , Siyuan Gu, Dawei Zhong, Lu Fang.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020 Sep 2.

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High-quality reconstruction of 3D geometry and texture plays a vital role in providing immersive perception of the real world. Additionally, online computation enables the practical usage of 3D reconstruction for interaction. We present an RGBD-based globally-consistent dense 3D reconstruction approach, where high-quality (i.e., the spatial resolution of the RGB image) texture patches are mapped on high-resolution geometric models online. The whole pipeline uses merely the CPU computing of a portable device. For real-time geometric reconstruction with online texturing, we propose to solve the texture optimization problem with a simplified incremental MRF solver in the context of geometric reconstruction pipeline using sparse voxel sampling strategy. An efficient reference-based color adjustment scheme is also proposed to achieve consistent texture patch colors under inconsistentluminance situations. Quantitative and qualitative experiments demonstrate that our online scheme achieves a realistic visualization of the environment with more abundant details, while taking fairly compact memory consumption and much lower computational complexity than existing solutions.



If you find our code useful, please kindly cite our paper:

  author={Han, Lei and Gu, Siyuan and Zhong, Dawei and Quan, Shuxue and Fang, Lu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Real-Time Globally Consistent Dense 3D Reconstruction With Online Texturing},